scholarly journals Discrete Element Simulation of the Effect of Roller-Spreading Parameters on Powder-Bed Density in Additive Manufacturing

Materials ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2285
Author(s):  
Jiangtao Zhang ◽  
Yuanqiang Tan ◽  
Tao Bao ◽  
Yangli Xu ◽  
Xiangwu Xiao ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Layer Thickness ◽  
Rotational Speed ◽  
High Performance ◽  
Ceramic Powder ◽  
Discrete Element ◽  
Parameters Optimization ◽  
Powder Layer ◽  
Translational Velocity ◽  
Powder Bed

The powder-bed with uniform and high density that determined by the spreading process parameters is the key factor for fabricating high performance parts in Additive Manufacturing (AM) process. In this work, Discrete Element Method (DEM) was deployed in order to simulate Al2O3 ceramic powder roller-spreading. The effects of roller-spreading parameters include translational velocity Vs, roller’s rotational speed ω, roller’s diameter D, and powder layer thickness H on powder-bed density were analyzed. The results show that the increased translational velocity of roller leads to poor powder-bed density. However, the larger roller’s diameter will improve powder-bed density. Moreover, the roller’s rotational speed has little effect on powder-bed density. Layer thickness is the most significant influencing factor on powder-bed density. When layer thickness is 50 μm, most of particles are pushed out of the build platform forming a lot of voids. However, when the layer thickness is greater than 150 μm, the powder-bed becomes more uniform and denser. This work can provide a reliable basis for roller-spreading parameters optimization.

On the measurement of effective powder layer thickness in laser powder-bed fusion additive manufacturing of metals

2018 ◽  
Vol 4 (2) ◽  
pp. 109-116 ◽  
Author(s):  
Yahya Mahmoodkhani ◽  
Usman Ali ◽  
Shahriar Imani Shahabad ◽  
Adhitan Rani Kasinathan ◽  
Reza Esmaeilizadeh ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Layer Thickness ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

scholarly journals Spreadability Testing of Powder for Additive Manufacturing

2021 ◽  
Vol 166 (1) ◽  
pp. 9-13
Author(s):  
Christopher Neil Hulme-Smith ◽  
Vignesh Hari ◽  
Pelle Mellin
Keyword(s):  
Additive Manufacturing ◽  
Layer Thickness ◽  
Measurement Procedure ◽  
Quantitative Information ◽  
Thin Layers ◽  
Spreading Speed ◽  
Powder Bed ◽  
Critical Step ◽  
Robust Image

AbstractThe spreading of powders into thin layers is a critical step in powder bed additive manufacturing, but there is no accepted technique to test it. There is not even a metric that can be used to describe spreading behaviour. A robust, image-based measurement procedure has been developed and can be implemented at modest cost and with minimal training. The analysis is automated to derive quantitative information about the characteristics of the spread layer. The technique has been demonstrated for three powders to quantify their spreading behaviour as a function of layer thickness and spreading speed.

Camera signal dependencies within coaxial melt pool monitoring in laser powder bed fusion

2020 ◽  
Vol 26 (1) ◽  
pp. 100-106 ◽  
Author(s):  
Tobias Kolb ◽  
Reza Elahi ◽  
Jan Seeger ◽  
Mathews Soris ◽  
Christian Scheitler ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Layer Thickness ◽  
Powder Layer ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Content Type ◽  
Powder Bed ◽  
Sensory Signals ◽  
Melt Pool ◽  
Research Activities

Purpose The purpose of this paper is to analyse the signal dependency of the camera-based coaxial monitoring system QMMeltpool 3D (Concept Laser GmbH, Lichtenfels, Germany) for laser powder bed fusion (LPBF) under the variation of process parameters, position, direction and layer thickness to determine the capability of the system. Because such and similar monitoring systems are designed and presented for quality assurance in series production, it is important to present the dominant signal influences and limitations. Design/methodology/approach Hardware of the commercially available coaxial monitoring QMMeltpool 3D is used to investigate the thermal emission of the interaction zone during LPBF. The raw images of the camera are analysed by means of image processing to bypass the software of QMMeltpool 3D and to gain a high level of signal understanding. Laser power, scan speed, laser spot diameter and powder layer thickness were varied for single-melt tracks to determine the influence of a parameter variation on the measured sensory signals. The effects of the scan direction and position were also analysed in detail. The influence of surface roughness on the detected sensory signals was simulated by a machined substrate plate. Findings Parameter variations are confirmed to be detectable. Because of strong directional and positional dependencies of the melt-pool monitoring signal a calibration algorithm is necessary. A decreasing signal is detected for increasing layer thickness. Surface roughness is identified as a dominating factor with major influence on the melt-pool monitoring signal exceeding other process flaws. Research limitations/implications This work was performed with the hardware of a commercially available QMMeltpool 3D system of an LPBF machine M2 of the company Concept Laser GmbH. The results are relevant for all melt-pool monitoring research activities connected to LPBF, as well as for end users and serial production. Originality/value Surface roughness has not yet been revealed as being one of the most important origins for signal deviations in coaxial melt-pool monitoring. To the best of the authors’ knowledge, the direct comparison of influences because of parameters and environment has not been published to this extent. The detection, evaluation and remelting of surface roughness constitute a plausible workflow for closed-loop control in LPBF.

scholarly journals Preliminary Characterization of Glass/Alumina Composite Using Laser Powder Bed Fusion (L-PBF) Additive Manufacturing

Materials ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2156 ◽  
Author(s):  
Byeong Hoon Bae ◽  
Jeong Woo Lee ◽  
Jae Min Cha ◽  
Il-Won Kim ◽  
Hyun-Do Jung ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
High Performance ◽  
High Efficiency ◽  
Xrd Analysis ◽  
Glass Frit ◽  
High Density ◽  
Al2o3 Content ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Improved Performance

Powder bed fusion (PBF) additive manufacturing (AM) is currently used to produce high-efficiency, high-density, and high-performance products for a variety of applications. However, existing AM methods are applicable only to metal materials and not to high-melting-point ceramics. Here, we develop a composite material for PBF AM by adding Al2O3 to a glass material using laser melting. Al2O3 and a black pigment are added to a synthesized glass frit for improving the composite strength and increased laser-light absorption, respectively. Our sample analysis shows that the glass melts to form a composite when the mixture is laser-irradiated. To improve the sintering density, we heat-treat the sample at 750 °C to synthesize a high-density glass frit composite. As per our X-ray diffraction (XRD) analysis to confirm the reactivity of the glass frit and Al2O3, we find that no reactions occur between glass and crystalline Al2O3. Moreover, we obtain a high sample density of ≥95% of the theoretical density. We also evaluate the composite’s mechanical properties as a function of the Al2O3 content. Our approach facilitates the manufacturing of ceramic 3D structures using glass materials through PBF AM and affords the benefits of reduced process cost, improved performance, newer functionalities, and increased value addition.

scholarly journals Analysis of Density, Roughness, and Accuracy of the Atomic Diffusion Additive Manufacturing (ADAM) Process for Metal Parts

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4122 ◽  
Author(s):  
Manuela Galati ◽  
Paolo Minetola
Keyword(s):  
Additive Manufacturing ◽  
Layer Thickness ◽  
Dimensional Accuracy ◽  
Raw Material ◽  
Atomic Diffusion ◽  
Powder Bed ◽  
Metal Parts ◽  
Unique System ◽  
Am Processes ◽  
Metal Additive

Atomic Diffusion Additive Manufacturing (ADAM) is a recent layer-wise process patented by Markforged for metals based on material extrusion. ADAM can be classified as an indirect additive manufacturing process in which a filament of metal powder encased in a plastic binder is used. After the fabrication of a green part, the plastic binder is removed by the post-treatments of washing and sintering (frittage). The aim of this work is to provide a preliminary characterisation of the ADAM process using Markforged Metal X, the unique system currently available on the market. Particularly, the density of printed 17-4 PH material is investigated, varying the layer thickness and the sample size. The dimensional accuracy of the ADAM process is evaluated using the ISO IT grades of a reference artefact. Due to the deposition strategy, the final density of the material results in being strongly dependent on the layer thickness and the size of the sample. The density of the material is low if compared to the material processed by powder bed AM processes. The superficial roughness is strongly dependent upon the layer thickness, but higher than that of other metal additive manufacturing processes because of the use of raw material in the filament form. The accuracy of the process achieves the IT13 grade that is comparable to that of traditional processes for the production of semi-finished metal parts.

Vision-Based Inspection System for Dimensional Accuracy in Powder-Bed Additive Manufacturing

2016 ◽  
Author(s):  
Masoumeh Aminzadeh ◽  
Thomas Kurfess
Keyword(s):  
Additive Manufacturing ◽  
Dimensional Accuracy ◽  
Powder Layer ◽  
Inspection System ◽  
Geometric Errors ◽  
Low Contrast ◽  
Powder Bed ◽  
Dimensional Precision ◽  
Geometric Objects

Laser powder-bed fusion (L-PBF) is an additive manufacturing (AM) process that enables fabrication of functional metal parts with near-net-shape geometries. The drawback to L-PBF is its lack of dimensional precision and accuracy. The efficiency of powder fusion process in powder-bed AM processes is highly affected by process errors, powder irregularities as well as geometric factors. Formation of defects such as lack of fusion and over-fusion due to the aforementioned factors causes dimensional errors that significantly damage the precision. This paper addresses the development of an automated in-situ inspection system for powder-bed additive manufacturing processes based on machine vision. The results of the in-situ automated inspection of dimensional accuracy allows for early identification of faulty parts or alternatively in-situ correction of geometric errors by taking appropriate corrective actions. In this inspection system, 2D optical images captured from each layer of the AM part during the build are analyzed and the geometric errors and defects impairing the dimensional accuracy are detected in each layer. To successfully detect geometric errors, fused geometric objects must be detected in the powder layer. Image processing algorithms are effectively designed to detect the geometric objects from images of low contrast captured during the build inside the chamber. The developed algorithms are implemented to a large number of test images and their performance and precision are evaluated quantitatively. The failure probabilities for the algorithms are also determined statistically.

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